scholarly journals An Italian Model for the Management of Cancer patients during COVID-19 pandemic: the Oncological Orientation region Center (COrO) of Taranto (ROP)

Author(s):  
Salvatore Pisconti ◽  
Gabriella Modoni ◽  
Concetta Cafiero ◽  
Giuseppe Simeone ◽  
Giammarco Surico ◽  
...  

Objective: The recent outbreak of COVID-19 caused a limitation of the resources of the National Health System and the necessity to formulate novel practice recommendation for oncological care. To date there are not available any management guidelines for cancer patients in case of pandemic. Each center has tried to manage its own needs and requests independently, often reducing access to treatment and diagnostic exams to patients. Here we have described the management of cancer patients during COVID-19 infection with suggestions of some practical approaches applied by our Regional Center for Oncological Orientation (COrO) in S.G. Moscati Hospital (Taranto-Italy). Subjects and Methods: Our strategies were the minimization of interruption of cancer treatment through the extension of Taranto's Health Regional (CorO). The extension of oncological network, assisted by the General Management of Taranto ASL through agreements with private structures in Taranto 's area allowed cancer patients to receive up to 11 different types of services, according to their needs(first investigation or follow up)and representing an exclusive organization on the entire Italian territory. Results: Thanks to the organization of the COrO in 2020, 1406 first oncological visits and 566 preparatory treatments were carried out, 372 of exemption for oncological pathology (free health care) were activated, 1742 instrumental investigations and 7 cases of civil invalidity were performed(certificate of disability). Conclusions: We have overcome the barriers to care of oncology patients that has led to a total reduction of waiting lists representing a practical application model that can be extended in to other healthcare settings.

2020 ◽  
pp. 204748732093087
Author(s):  
Alexander Fardman ◽  
Gabriel D Banschick ◽  
Razi Rabia ◽  
Ruth Percik ◽  
Dana Fourey ◽  
...  

Aims Data on the association of cardiorespiratory fitness with survival of cancer patients are limited. This study aimed to evaluate the association between midlife cardiorespiratory fitness and survival after a subsequent cancer diagnosis. Methods We evaluated 19,134 asymptomatic self-referred adults who were screened in preventive healthcare settings. All subjects were free of cardiovascular disease and cancer at baseline and completed a maximal exercise stress test. Fitness was categorised into age-specific and sex-specific quintiles according to the treadmill time and dichotomised to low (quintiles 1–2) and high fitness groups. Results The mean age was 50 ± 8 years and 72% were men. During a median follow-up of 13 years (interquartile range 7–16) 517 (3%) died. Overall, 1455 (7.6%) subjects developed cancer with a median time to cancer diagnosis of 6.4 years (interquartile range 3–10). Death from the time of cancer diagnosis was significantly lower among the high fitness group ( Plog rank = 0.03). Time-dependent analysis showed that subjects who developed cancer during follow-up were more likely to die ( P < 0.001). The association of cancer with survival was fitness dependent such that in the lower fitness group cancer was associated with a higher risk of death, whereas among the high fitness group the risk of death was lower (hazard ratio 20 vs. 15; Pfor interaction = 0.047). The effect modification persisted after applying a 4-year blanking period between fitness assessment and cancer diagnosis ( Pfor interaction = 0.003). Conclusion Higher midlife cardiorespiratory fitness is associated with better survival among cancer patients. Our findings support fitness assessment in preventive healthcare settings.


2004 ◽  
Vol 171 (4S) ◽  
pp. 194-195
Author(s):  
Kyoichi Tomita ◽  
Haruki Kume ◽  
Keishi Kashibuchi ◽  
Satoru Muto ◽  
Shigeo Horie ◽  
...  

2020 ◽  
Vol 16 (32) ◽  
pp. 2635-2643
Author(s):  
Samantha L Freije ◽  
Jordan A Holmes ◽  
Saleh Rachidi ◽  
Susannah G Ellsworth ◽  
Richard C Zellars ◽  
...  

Aim: To identify demographic predictors of patients who miss oncology follow-up, considering that missed follow-up has not been well studies in cancer patients. Methods: Patients with solid tumors diagnosed from 2007 to 2016 were analyzed (n = 16,080). Univariate and multivariable logistic regression models were constructed to examine predictors of missed follow-up. Results: Our study revealed that 21.2% of patients missed ≥1 follow-up appointment. African–American race (odds ratio [OR] 1.33; 95% CI: 1.17–1.51), Medicaid insurance (OR 1.59; 1.36–1.87), no insurance (OR 1.66; 1.32–2.10) and rural residence (OR 1.78; 1.49–2.13) were associated with missed follow-up. Conclusion: Many cancer patients miss follow-up, and inadequate follow-up may influence cancer outcomes. Further research is needed on how to address disparities in follow-up care in high-risk patients.


Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2632
Author(s):  
Aparajita Budithi ◽  
Sumeyye Su ◽  
Arkadz Kirshtein ◽  
Leili Shahriyari

Many colon cancer patients show resistance to their treatments. Therefore, it is important to consider unique characteristic of each tumor to find the best treatment options for each patient. In this study, we develop a data driven mathematical model for interaction between the tumor microenvironment and FOLFIRI drug agents in colon cancer. Patients are divided into five distinct clusters based on their estimated immune cell fractions obtained from their primary tumors’ gene expression data. We then analyze the effects of drugs on cancer cells and immune cells in each group, and we observe different responses to the FOLFIRI drugs between patients in different immune groups. For instance, patients in cluster 3 with the highest T-reg/T-helper ratio respond better to the FOLFIRI treatment, while patients in cluster 2 with the lowest T-reg/T-helper ratio resist the treatment. Moreover, we use ROC curve to validate the model using the tumor status of the patients at their follow up, and the model predicts well for the earlier follow up days.


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